{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 蒙特卡罗方法\n",
    "\n",
    "使用蒙特卡罗方法求卡方分布的分位数\n",
    "\n",
    "## 测试numpy和plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1df57d2dc40>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x_data = np.arange(500)\n",
    "y_data = np.random.normal(loc=0, scale=1, size=(500))\n",
    "\n",
    "plt.scatter(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1df57d9e1f0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "power_y_data = np.square(y_data)\n",
    "\n",
    "plt.scatter(x_data, power_y_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算卡方分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1df57e04880>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = np.arange(5000)\n",
    "y_data = np.random.normal(loc=0, scale=1, size=(5000, 3))\n",
    "y_data = np.square(y_data)\n",
    "y_data = np.sum(y_data, axis=1)\n",
    "\n",
    "plt.scatter(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489\n"
     ]
    }
   ],
   "source": [
    "y_data = np.count_nonzero(y_data > 6.25)\n",
    "print(y_data)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "de179aa84221d24dc7443e7821ea96dfbb760331c0f9adb43940e4c427033418"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit ('venv': venv)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
